Evolving clustering algorithms for wireless sensor networks with various radiation patterns to reduce energy consumption

Energy consumption affects Wireless Sensor Networks (WSNs) lifetime and may cause network degradation. Potential work has been focused on consumed energy reduction techniques. The consumed energy during communication is affected exponentially by the distance between the communicating nodes; the more communication distance between two nodes the more energy consumed. Clustering was used to help in reducing the energy consumed in the wireless data transmission. Clustering gathers the nodes into groups called clusters. One node from each cluster is elected to be the cluster head (CH). Deciding the optimal number of clusters and which sensors should be CHs is a challenge problem. We presented two hybrid clustering algorithms called K-Means Particle Swarm Optimization (KPSO) and K-Means Genetic Algorithms (KGAs) in [1], [2] with significant improvement over traditional Low Energy Adaptive clustering Hierarchy protocol (LEACH). Considering the various antenna patterns for WSN we were able to improve the clustering algorithm performance in energy saving. In this article, we shall review our presented algorithms and present in details the new antenna pattern design based PSO and GAs.